Coordinated Motion Planning: Reconfiguring a Swarm of Labeled Robots with Bounded Stretch

Authors Erik D. Demaine, Sándor P. Fekete, Phillip Keldenich, Christian Scheffer, Henk Meijer



PDF
Thumbnail PDF

File

LIPIcs.SoCG.2018.29.pdf
  • Filesize: 0.56 MB
  • 15 pages

Document Identifiers

Author Details

Erik D. Demaine
Sándor P. Fekete
Phillip Keldenich
Christian Scheffer
Henk Meijer

Cite AsGet BibTex

Erik D. Demaine, Sándor P. Fekete, Phillip Keldenich, Christian Scheffer, and Henk Meijer. Coordinated Motion Planning: Reconfiguring a Swarm of Labeled Robots with Bounded Stretch. In 34th International Symposium on Computational Geometry (SoCG 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 99, pp. 29:1-29:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)
https://doi.org/10.4230/LIPIcs.SoCG.2018.29

Abstract

We present a number of breakthroughs for coordinated motion planning, in which the objective is to reconfigure a swarm of labeled convex objects by a combination of parallel, continuous, collision-free translations into a given target arrangement. Problems of this type can be traced back to the classic work of Schwartz and Sharir (1983), who gave a method for deciding the existence of a coordinated motion for a set of disks between obstacles; their approach is polynomial in the complexity of the obstacles, but exponential in the number of disks. Despite a broad range of other non-trivial results for multi-object motion planning, previous work has largely focused on sequential schedules, in which one robot moves at a time, with objectives such as the number of moves; attempts to minimize the overall makespan of a coordinated parallel motion schedule (with many robots moving simultaneously) have defied all attempts at establishing the complexity in the absence of obstacles, as well as the existence of efficient approximation methods. We resolve these open problems by developing a framework that provides constant-factor approximation algorithms for minimizing the execution time of a coordinated, parallel motion plan for a swarm of robots in the absence of obstacles, provided their arrangement entails some amount of separability. In fact, our algorithm achieves constant stretch factor: If all robots want to move at most d units from their respective starting positions, then the total duration of the overall schedule (and hence the distance traveled by each robot) is O(d). Various extensions include unlabeled robots and different classes of robots. We also resolve the complexity of finding a reconfiguration plan with minimal execution time by proving that this is NP-hard, even for a grid arrangement without any stationary obstacles. On the other hand, we show that for densely packed disks that cannot be well separated, a stretch factor Omega(N^{1/4}) may be required. On the positive side, we establish a stretch factor of O(N^{1/2}) even in this case. The intricate difficulties of computing precise optimal solutions are demonstrated by the seemingly simple case of just two disks, which is shown to be excruciatingly difficult to solve to optimality.
Keywords
  • Robot swarms
  • coordinated motion planning
  • parallel motion
  • makespan
  • bounded stretch
  • complexity
  • approximation

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads

References

  1. M. Abellanas, S. Bereg, F. Hurtado, A. G. Olaverri, D. Rappaport, and J. Tejel. Moving coins. Computational Geometry: Theory and Applications, 34(1):35-48, 2006. Google Scholar
  2. Aviv Adler, Mark de Berg, Dan Halperin, and Kiril Solovey. Efficient multi-robot motion planning for unlabeled discs in simple polygons. IEEE Transactions on Automation Science and Engineering, 12(4):1309-1317, 2015. Google Scholar
  3. B. Aronov, M. de Berg, A. F. van der Stappen, P. Švestka, and J. Vleugels. Motion planning for multiple robots. Discrete &Computational Geometry, 22(4):505-525, 1999. Google Scholar
  4. Aaron T. Becker, Sándor P. Fekete, Phillip Keldenich, Lillian Lin, and Christian Scheffer. Coordinated motion planning: The video. In Csaba Tóth and Bettina Speckmann, editors, 34th International Symposium on Computational Geometry (SoCG 2018, these proceedings), volume 99 of Leibniz International Proceedings in Informatics (LIPIcs), pages 74:1-74:6, 2018. Video available via https://youtu.be/0OrG72sX5gk. Google Scholar
  5. S. Bereg, A. Dumitrescu, and J. Pach. Sliding disks in the plane. International Journal of Computational Geometry & Applications, 18(5):373-387, 2008. Google Scholar
  6. Erik D. Demaine, Sándor P. Fekete, Phillip Keldenich, Henk Meijer, and Christian Scheffer. Coordinated motion planning: Reconfiguring a swarm of labeled robots with bounded stretch. Computing Research Repository (CoRR), 1801:1-32, 2018. Available at https://arxiv.org/abs/1801.01689. Google Scholar
  7. A. Dumitrescu and M. Jiang. On reconfiguration of disks in the plane and related problems. Computational Geometry: Theory and Applications, 46:191-202, 2013. Google Scholar
  8. G. W. Flake and E. B. Baum. Rush Hour is PSPACE-complete, or “Why you should generously tip parking lot attendants”. Theoretical Computer Science, 270(1):895-911, 2002. Google Scholar
  9. R. A. Hearn and E. D. Demaine. PSPACE-completeness of sliding-block puzzles and other problems through the nondeterministic constraint logic model of computation. Theoretical Computer Science, 343(1):72-96, 2005. Google Scholar
  10. R. A. Hearn and E. D. Demaine. Games, puzzles, and computation. CRC Press, 2009. Google Scholar
  11. J. E. Hopcroft, J. T. Schwartz, and M. Sharir. On the complexity of motion planning for multiple independent objects; PSPACE-hardness of the warehouseman’s problem. International Journal of Robotics Research, 3(4):76-88, 1984. Google Scholar
  12. J. E. Hopcroft and G. T. Wilfong. Reducing multiple object motion planning to graph searching. SIAM Journal on Computing, 15(3):768-785, 1986. Google Scholar
  13. S. Kloder and S. Hutchinson. Path planning for permutation-invariant multi-robot formations. In IEEE Transactions on Robotics, volume 22, pages 650-665. IEEE, 2006. Google Scholar
  14. D. Kornhauser, G. Miller, and P. Spirakis. Coordinating pebble motion on graphs, the diameter of permutation groups, and applications. In Proceedings of the 25th Annual Symposium on Foundations of Computer Science (SFCS), pages 241-250, 1984. URL: http://dx.doi.org/10.1109/SFCS.1984.715921.
  15. J. M. Marberg and E. Gafni. Sorting in constant number of row and column phases on a mesh. Algorithmica, 3:561-572, 1988. URL: http://dx.doi.org/10.1007/BF01762132.
  16. Mark Overmars. Contributed open problem. In Sándor P. Fekete, Rudolf Fleischer, Rolf Klein, and Alejandro Lopez-Ortiz, editors, Robot Navigation, Dagstuhl Seminar 06421, 2006. URL: http://www.dagstuhl.de/de/programm/kalender/semhp/?semnr=06421.
  17. G. Ramanathan and V. Alagar. Algorithmic motion planning in robotics: Coordinated motion of several disks amidst polygonal obstacles. In Proceedings of the Second IEEE International Conference on Robotics and Automation (ICRA), volume 2, pages 514-522, 1985. Google Scholar
  18. D. Ratner and M. K. Warmuth. Finding a shortest solution for the N × N extension of the 15-puzzle is intractable. In Proceedings of the Fifth AAAI Conference on Artificial Intelligence, pages 168-172, 1986. Google Scholar
  19. Christian Scheideler. Universal routing strategies for interconnection networks, volume 1390 of Lecture Notes in Computer Science. Springer, 1998. Google Scholar
  20. Jacob T. Schwartz and M. Sharir. On the piano movers' problem: III. Coordinating the motion of several independent bodies: the special case of circular bodies moving amidst polygonal barriers. International Journal of Robotics Research, 2(3):46-75, 1983. Google Scholar
  21. K. Solovey and D. Halperin. k-color multi-robot motion planning. International Journal of Robotics Research, 33(1):82-97, 2014. Google Scholar
  22. K. Solovey, J. Yu, O. Zamir, and D. Halperin. Motion planning for unlabeled discs with optimality guarantees. In Robotics: Science and Systems (RSS), 2015. Google Scholar
  23. Kiril Solovey and Dan Halperin. On the hardness of unlabeled multi-robot motion planning. International Journal of Robotics Research, 35(14):1750-1759, 2016. Google Scholar
  24. P. Spirakis and C. K. Yap. Strong NP-hardness of moving many discs. Information Processing Letters, 19(1):55-59, 1984. Google Scholar
  25. M. Turpin, N. Michael, and V. Kumar. Trajectory planning and assignment in multirobot systems. In Algorithmic Foundations of Robotics X, pages 175-190. Springer, 2013. Google Scholar
  26. R. M. Wilson. Graph puzzles, homotopy, and the alternating group. Journal of Combinatorial Theory, Series B, 16(1):86-96, 1974. Google Scholar
  27. J. Yu. Constant factor optimal multi-robot path planning in well-connected environments. arXiv. URL: https://arxiv.org/abs/1706.07255.
  28. J. Yu. Constant factor time optimal multi-robot routing on high-dimensional grids in mostly sub-quadratic time. arXiv. URL: https://arxiv.org/abs/1801.10465.
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail